Machine Learning Engineer for Generative AI Solution Architect

at  Capgemini

JOB DESCRIPTION

As a Senior GenAI Solutions Machine Learning Engineer, you will lead the development and implementation of advanced data engineering solutions to support the deployment and optimization of Generative AI models. Your role will involve leveraging your extensive experience to design robust, scalable, and innovative data architectures that align with the unique requirements of General Artificial Intelligence (GenAI) applications.

REQUIRED SKILLS

• Bachelor’s degree in computer science, data engineering, or a related field with 5+ years experience (Master’s preferred).
• Proven experience in data engineering, MLOps, ETL, and database management, QL and data manipulation languages.
• Azure, Python, Java, or Scala.
• data warehousing platforms (e.g., Databricks, Amazon Redshift, Snowflake) and big data technologies (e.g., Hadoop, Spark).
• highly scalable Data stores, Data Lake, Data Warehouse, Lakehouse, and unstructured datasets

• The Machine Learning Engineer will be responsible for architectural design and planning, advanced data pipelines, model integration and optimization, scalability, performance and research and innovation supporting production generative AI systems.
• Production level ML workloads for customers using Databricks platform, including end-to-end ML pipelines, training/inference optimization, integration with cloud-native services and MLOps
• Build and maintain data engineering solutions on cloud platforms using hyperscaler services.
• Develop production-grade cloud (AWS/Azure/GCP) infrastructure that supports the deployment of ML applications, including drift monitoring
• Design, develop, and maintain data pipelines to efficiently collect, process, and load data from various sources into data storage systems (e.g., data warehouses, data lakes).
• Understanding of indexing and vectorization to use with Generative AI prompt engineering.
• Strong understanding of fundamental data science concepts in NLP, including selection and understanding of embedding models.
• Use hyperscaler technologies to support data needs for expansion of Machine Learning/Data Science capabilities including generative AI.
• Design, develop, and implement scalable data pipelines and ETL/ELT processes using Python, PySpark and API integrations.